Cross-Correlation Based Automatic Segmentation of Medial Phonemes

Autor:	Vasile Stoicu-Tivadar, Stelian Nicola, Emilian-Erman Mahmut
Rok vydání:	2020
Předmět:	Statistical classification education.field_of_study Speech recognition Speech sound disorder Population medicine Subject (documents) Pronunciation Speech processing medicine.disease education Utterance Word (computer architecture)
Zdroj:	2020 International Symposium on Electronics and Telecommunications (ISETC).
DOI:	10.1109/isetc50328.2020.9301048
Popis:	This paper describes a cross-correlation based method aimed at extracting homogeneously-trimmed target medial phonemes from a reference pronunciation (a word or logatome pronounced by the Speech Language Pathologist (SLP)) and a sample utterance (the same word/logatome pronounced by a subject). The newly-generated audio segments are then fed to an Information Entropy based classification stage in order to assess the (dis)similarities between them and to serve as a valid, automated Speech Sound Disorder (SSD) Screening tool. The input for the study consisted in audio recordings of a Romanian word containing the target phoneme /r/ in medial position pronounced by a population of 44 preschoolers and primary schoolers, aged 6-7.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1bc0a6daf340419f8b58fa8bac5cb9a6 https://doi.org/10.1109/isetc50328.2020.9301048 Zobrazit plný text záznamu